MQL5 Programming Articles

The article guides you through building a real‑time Hilbert Spectrum indicator in MQL5: EMD produces IMFs, a discrete FIR Hilbert transform yields analytical signals, and a CCanvas renderer draws a time–frequency–energy heatmap. Implementation details include extrema detection, SD stopping, phase unwrapping, Nyquist clipping, and bin accumulation. Use the panel to identify active cycle bands and regime shifts while keeping trade rules separate.

BUZOA is a population-based metaheuristic algorithm in which each agent randomly selects one of three hunting tactics at each iteration: a narrow search around its personal best, a classic PSO step toward the flock leader, or full teleportation to a random point in the search space. The article examines the implementation of the algorithm in MQL5, identifies a coefficient sign error in the original formulation, and presents benchmark results on a standard test bench.

This part turns stored account references into a financial monitoring engine for MetaTrader 5. It calculates daily drawdown from the confirmed session balance, overall drawdown from the initial challenge balance with a static equity floor, and profit‑target progress from realized balance. The EA derives usage, remaining allowance, and status for each rule, then aggregates them into a single guard state where risk conditions override target completion.

The article implements a deterministic MQL5 detector for head and shoulders and its inverse, validating structure from swing labels (HH, LH, HL, LL) rather than raw peak geometry. Seven ATR-aware checks enforce time order, pattern span, head clearance, shoulder symmetry, and height. A three-state lifecycle separates formation from neckline break, locks a setup, and prevents re-entry. Entries trigger only on a chart-timeframe close through the neckline.

Build and deploy a conservative, offline‑trained policy entirely in MQL5. Logged trades become a fixed dataset for a two‑layer Q‑network trained with Conservative Q‑Learning, which pushes down Q‑values for poorly supported actions to limit extrapolation error. The EA loads the frozen weights and applies an out‑of‑distribution guard using saved feature statistics. This yields reproducible training and execution without live exploration.

Build a responsive session volume profile indicator in MQL5 from tick data with draggable session bounds and dual anchoring. The article details hash-based bucket aggregation for historical and live ticks, a timer-driven rendering loop with partial updates, automatic or custom bucket sizing, pixel-anchored proportional bars, POC highlighting, and multi‑instance safety—so you can implement, optimize, and run multiple profiles on one chart without UI lag.

This article benchmarks one MLP forward pass implemented three ways in MQL5: native code, ALGLIB, and ONNX Runtime. We quantify numerical fidelity versus a float64 ground truth and execution latency (mean/percentiles), then outline practical criteria and a ready‑to‑use harness to help you choose and integrate the right engine in an EA.

Metaprompting is an approach in which the LLM itself optimizes trading instructions based on actual P&L and signal quality metrics. The article demonstrates a practical implementation in Python and MQL5: a prompt version registry, an execution agent, an evaluator for directional accuracy and profit factor, and a meta-LLM that generates improvements in a loop. The solution is integrated into the Expert Advisor without interrupting trading.